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        <span>数学建模问题3</span>
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                <a href="/2020/09/25/python%20work/%E6%95%B0%E5%AD%A6%E5%BB%BA%E6%A8%A1%E9%97%AE%E9%A2%983/">数学建模问题3</a>
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                <i class="fa fa-calendar"></i> 2020-09-25
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        <h1 id="数学建模问题3"><a href="#数学建模问题3" class="headerlink" title=" 数学建模问题3"></a> 数学建模问题3</h1><p>问题三：在P300脑-机接口系统中，往往需要花费很长时间获取有标签样本来训练模型。为了减少训练时间，请根据附件1所给数据，选择适量的样本作为有标签样本，其余训练样本作为无标签样本，在问题二所得一组最优通道组合的基础上，设计一种学习的方法，并利用问题二的测试数据（char13-char17）检验方法的有效性，同时利用所设计的学习方法找出测试集中的其余待识别目标（char18-char22）。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span 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class="line">122</span><br><span class="line">123</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/9/20</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"><span class="keyword">from</span> sklearn <span class="keyword">import</span> preprocessing</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> accuracy_score, cohen_kappa_score</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestRegressor</span><br><span class="line"><span class="keyword">import</span> scipy.io <span class="keyword">as</span> scio</span><br><span class="line"><span class="keyword">from</span> sklearn.externals <span class="keyword">import</span> joblib</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 整体数据预测</span></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&#x27;ignore&#x27;</span>)</span><br><span class="line"></span><br><span class="line">text = <span class="string">&#x27;S1&#x27;</span></span><br><span class="line">num = <span class="number">13</span></span><br><span class="line">nums = num - <span class="number">13</span></span><br><span class="line"><span class="comment"># S1的训练数据进行标准化</span></span><br><span class="line">feature = pd.read_excel(<span class="string">&quot;E:/EEG数据/label/&quot;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&quot;_std_train_data.xlsx&quot;</span>)</span><br><span class="line">all_std = preprocessing.scale(np.array(feature.iloc[:, <span class="number">1</span>:]).tolist())</span><br><span class="line"></span><br><span class="line"><span class="comment"># S1对应的标签</span></span><br><span class="line">labels = pd.read_excel(<span class="string">&quot;E:/EEG数据/label/&quot;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&quot;_all_train_label.xlsx&quot;</span>)[<span class="string">&#x27;label&#x27;</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 提取测试集</span></span><br><span class="line"></span><br><span class="line">test_data = pd.read_excel(<span class="string">&quot;E:/EEG数据/&quot;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&quot;/%s&quot;</span> % text + <span class="string">&quot;_test_data.xlsx&quot;</span>, header=<span class="literal">None</span>,</span><br><span class="line">                          sheet_name=<span class="string">&#x27;char&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % num)</span><br><span class="line">std_test = preprocessing.scale(np.array(test_data).tolist())</span><br><span class="line"></span><br><span class="line">test_event = pd.read_excel(<span class="string">&#x27;E:/EEG数据/&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % text + <span class="string">&#x27;/%s&#x27;</span> % text + <span class="string">&#x27;_test_event.xlsx&#x27;</span>, header=<span class="literal">None</span>,</span><br><span class="line">                           sheet_name=<span class="string">&#x27;char&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % num)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 提取avg_epoch中的数据</span></span><br><span class="line">dataFile = <span class="string">&#x27;E:/EEG数据/locs_data/&#x27;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&#x27;_test_locs.mat&#x27;</span></span><br><span class="line">all_datas = scio.loadmat(dataFile)[<span class="string">&#x27;all_sheet&#x27;</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 提取avg_epoch中的数据</span></span><br><span class="line">dataFiles = <span class="string">&#x27;E:/EEG数据/locs_data/&#x27;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&#x27;_test_avg_locs.mat&#x27;</span></span><br><span class="line">avg_datas = scio.loadmat(dataFiles)[<span class="string">&#x27;avg_locs&#x27;</span>]</span><br><span class="line"></span><br><span class="line">X_train, X_test, Y_train, Y_test = train_test_split(all_std, labels,</span><br><span class="line">                                                    stratify=labels, test_size=<span class="number">0.3</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">clf = RandomForestRegressor()</span><br><span class="line">clf.fit(X_train, Y_train)</span><br><span class="line">Y_pred = clf.predict(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 去预测test和train</span></span><br><span class="line">pred_y = clf.predict(std_test)</span><br><span class="line"></span><br><span class="line">locs = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(pred_y)):</span><br><span class="line">    <span class="keyword">if</span> pred_y[i] &gt; <span class="number">0.5</span>:</span><br><span class="line">        locs.append(i)</span><br><span class="line"></span><br><span class="line">train_event = test_event</span><br><span class="line">char = []</span><br><span class="line">char_f = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">66</span>):</span><br><span class="line">    <span class="keyword">if</span> train_event[<span class="number">0</span>][i] &gt;= <span class="number">100</span>:</span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        char.append(train_event[<span class="number">1</span>][i])</span><br><span class="line">        char_f.append(train_event[<span class="number">0</span>][i])</span><br><span class="line"></span><br><span class="line"><span class="comment"># 得到想要的标签</span></span><br><span class="line">list_event = []</span><br><span class="line">list_locs = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(locs)):</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(char)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">80</span> &lt;= locs[i] - char[j] &lt; <span class="number">120</span>:</span><br><span class="line">            list_event.append(char_f[j])</span><br><span class="line">            list_locs.append(char[j])</span><br><span class="line">print(<span class="built_in">list</span>(<span class="built_in">set</span>(list_event)))</span><br><span class="line"><span class="comment"># 得到想要的mat文件中的数据</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line">fff = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(avg_datas[nums][<span class="number">0</span>][<span class="number">0</span>])):</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(locs)):</span><br><span class="line">        <span class="keyword">if</span> <span class="built_in">abs</span>(locs[j] - avg_datas[nums][<span class="number">0</span>][<span class="number">0</span>][i]) &lt;= <span class="number">20</span>:</span><br><span class="line">            fff.append(locs[j])</span><br><span class="line"></span><br><span class="line">list_event1 = []</span><br><span class="line">list_locs1 = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(fff)):</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(char)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">80</span> &lt;= fff[i] - char[j] &lt; <span class="number">120</span>:</span><br><span class="line">            list_event1.append(char_f[j])</span><br><span class="line">            list_locs1.append(char[j])</span><br><span class="line">print(<span class="built_in">list</span>(<span class="built_in">set</span>(list_event1)))</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">ffsh = []</span><br><span class="line"><span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">20</span>):</span><br><span class="line">    fsh = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_datas[nums][k][<span class="number">0</span>])):</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(locs)):</span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">abs</span>(locs[j] - all_datas[nums][k][<span class="number">0</span>][i]) &lt;= <span class="number">20</span>:</span><br><span class="line">                fsh.append(locs[j])</span><br><span class="line">    ffsh.append(fsh)</span><br><span class="line"></span><br><span class="line">all_event = []</span><br><span class="line"><span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(ffsh)):</span><br><span class="line">    list_event2 = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(ffsh[k])):</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(char)):</span><br><span class="line">            <span class="keyword">if</span> <span class="number">80</span> &lt;= ffsh[k][i] - char[j] &lt; <span class="number">120</span>:</span><br><span class="line">                list_event2.append(char_f[j])</span><br><span class="line">    all_event.append(list_event2)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># joblib.dump(clf, &quot;s1_rfreg_model.pkl&quot;)</span></span><br><span class="line"></span><br></pre></td></tr></table></figure>





<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/9/20</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"><span class="keyword">from</span> sklearn <span class="keyword">import</span> preprocessing</span><br><span class="line"><span class="keyword">import</span> scipy.io <span class="keyword">as</span> scio</span><br><span class="line"><span class="keyword">from</span> sklearn.externals <span class="keyword">import</span> joblib</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 整体数据预测</span></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&#x27;ignore&#x27;</span>)</span><br><span class="line"></span><br><span class="line">rf_model = joblib.load(<span class="string">&quot;s5_rfreg_model.pkl&quot;</span>)</span><br><span class="line">text = <span class="string">&#x27;S5&#x27;</span></span><br><span class="line">num = <span class="number">20</span></span><br><span class="line">print(num)</span><br><span class="line">nums = num - <span class="number">13</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># S1对应的标签</span></span><br><span class="line">labels = pd.read_excel(<span class="string">&quot;E:/EEG数据/label/&quot;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&quot;_all_train_label.xlsx&quot;</span>)[<span class="string">&#x27;label&#x27;</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 提取测试集</span></span><br><span class="line"></span><br><span class="line">test_data = pd.read_excel(<span class="string">&quot;E:/EEG数据/&quot;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&quot;/%s&quot;</span> % text + <span class="string">&quot;_test_data.xlsx&quot;</span>, header=<span class="literal">None</span>,</span><br><span class="line">                          sheet_name=<span class="string">&#x27;char&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % num)</span><br><span class="line">std_test = preprocessing.scale(np.array(test_data).tolist())</span><br><span class="line"></span><br><span class="line">test_event = pd.read_excel(<span class="string">&#x27;E:/EEG数据/&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % text + <span class="string">&#x27;/%s&#x27;</span> % text + <span class="string">&#x27;_test_event.xlsx&#x27;</span>, header=<span class="literal">None</span>,</span><br><span class="line">                           sheet_name=<span class="string">&#x27;char&#x27;</span> + <span class="string">&#x27;%d&#x27;</span> % num)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 提取avg_epoch中的数据</span></span><br><span class="line">dataFile = <span class="string">&#x27;E:/EEG数据/locs_data/&#x27;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&#x27;_test_locs.mat&#x27;</span></span><br><span class="line">all_datas = scio.loadmat(dataFile)[<span class="string">&#x27;all_sheet&#x27;</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 提取avg_epoch中的数据</span></span><br><span class="line">dataFiles = <span class="string">&#x27;E:/EEG数据/locs_data/&#x27;</span> + <span class="string">&quot;%s&quot;</span> % text + <span class="string">&#x27;_test_avg_locs.mat&#x27;</span></span><br><span class="line">avg_datas = scio.loadmat(dataFiles)[<span class="string">&#x27;avg_locs&#x27;</span>]</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">pred_y = rf_model.predict(std_test)</span><br><span class="line"></span><br><span class="line">locs = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(pred_y)):</span><br><span class="line">    <span class="keyword">if</span> pred_y[i] &gt; <span class="number">0.5</span>:</span><br><span class="line">        locs.append(i)</span><br><span class="line"></span><br><span class="line">train_event = test_event</span><br><span class="line">char = []</span><br><span class="line">char_f = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">66</span>):</span><br><span class="line">    <span class="keyword">if</span> train_event[<span class="number">0</span>][i] &gt;= <span class="number">100</span>:</span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        char.append(train_event[<span class="number">1</span>][i])</span><br><span class="line">        char_f.append(train_event[<span class="number">0</span>][i])</span><br><span class="line"></span><br><span class="line"><span class="comment"># 得到想要的标签</span></span><br><span class="line">list_event = []</span><br><span class="line">list_locs = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(locs)):</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(char)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">80</span> &lt;= locs[i] - char[j] &lt; <span class="number">120</span>:</span><br><span class="line">            list_event.append(char_f[j])</span><br><span class="line">            list_locs.append(char[j])</span><br><span class="line">print(<span class="built_in">list</span>(<span class="built_in">set</span>(list_event)))</span><br><span class="line"><span class="comment"># 得到想要的mat文件中的数据</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line">fff = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(avg_datas[nums][<span class="number">0</span>][<span class="number">0</span>])):</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(locs)):</span><br><span class="line">        <span class="keyword">if</span> <span class="built_in">abs</span>(locs[j] - avg_datas[nums][<span class="number">0</span>][<span class="number">0</span>][i]) &lt;= <span class="number">20</span>:</span><br><span class="line">            fff.append(locs[j])</span><br><span class="line"></span><br><span class="line">list_event1 = []</span><br><span class="line">list_locs1 = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(fff)):</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(char)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">80</span> &lt;= fff[i] - char[j] &lt; <span class="number">120</span>:</span><br><span class="line">            list_event1.append(char_f[j])</span><br><span class="line">            list_locs1.append(char[j])</span><br><span class="line">print(<span class="built_in">list</span>(<span class="built_in">set</span>(list_event1)))</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">ffsh = []</span><br><span class="line"><span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">20</span>):</span><br><span class="line">    fsh = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_datas[nums][k][<span class="number">0</span>])):</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(locs)):</span><br><span class="line">            <span class="keyword">if</span> <span class="built_in">abs</span>(locs[j] - all_datas[nums][k][<span class="number">0</span>][i]) &lt;= <span class="number">20</span>:</span><br><span class="line">                fsh.append(locs[j])</span><br><span class="line">    ffsh.append(fsh)</span><br><span class="line"></span><br><span class="line">all_event = []</span><br><span class="line"><span class="keyword">for</span> k <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(ffsh)):</span><br><span class="line">    list_event2 = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(ffsh[k])):</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(char)):</span><br><span class="line">            <span class="keyword">if</span> <span class="number">80</span> &lt;= ffsh[k][i] - char[j] &lt; <span class="number">120</span>:</span><br><span class="line">                list_event2.append(char_f[j])</span><br><span class="line">    all_event.append(list_event2)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<p>最后结合输出</p>
<p><img src="../images/timg-1601019488724.jpg"></p>

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